Abstract: Flight delays have significant implications for both travelers and the aviation industry. In this project, we address the challenge of predicting flight delays by harnessing the power of machine learning regression algorithms. By analyzing historical flight data and relevant influencing factors, we aim to provide accurate estimates of delay times for specific flights. The project begins with a comprehensive collection and pre-processing of relevant data, including departure and arrival times, weather conditions, airport congestion, and historical delay records. Feature engineering techniques are employed to extract valuable insights from the raw data, enhancing the predictive capabilities of the models. Several machine learning regression algorithms are explored, including Linear Regression, Support Vector Regression (SVR), and Decision Tree Regression. The selection of these algorithms is based on their suitability for capturing complex relationships and patterns within the data. Hyper parameter tuning and model evaluation are conducted rigorously to ensure optimal model performance. Evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are employed to quantify the accuracy of the predictions The contribution to our calculation is columns of highlight vector like flight date, take-off delay, separation between the two air terminals, planned appearance time and so forth We at that point use choice tree classifier to foresee if the flight appearance will be delayed or not. Besides, we compare decision tree classifier and calculated.

| DOI: 10.17148/IARJSET.2023.10850